Local Contrast as an Effective Means to Robust Clustering Against Varying Densities
Abstract
Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP.
Cite
Text
Chen et al. "Local Contrast as an Effective Means to Robust Clustering Against Varying Densities." Machine Learning, 2018. doi:10.1007/S10994-017-5693-XMarkdown
[Chen et al. "Local Contrast as an Effective Means to Robust Clustering Against Varying Densities." Machine Learning, 2018.](https://mlanthology.org/mlj/2018/chen2018mlj-local/) doi:10.1007/S10994-017-5693-XBibTeX
@article{chen2018mlj-local,
title = {{Local Contrast as an Effective Means to Robust Clustering Against Varying Densities}},
author = {Chen, Bo and Ting, Kai Ming and Washio, Takashi and Zhu, Ye},
journal = {Machine Learning},
year = {2018},
pages = {1621-1645},
doi = {10.1007/S10994-017-5693-X},
volume = {107},
url = {https://mlanthology.org/mlj/2018/chen2018mlj-local/}
}